FastqPuri: high-performance preprocessing of RNA-seq data

Abstract

Background: RNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.

Results: We here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness.

Conclusions: FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri . It is implemented in C and R and licensed under GPL v3.

Keywords: Preprocessing; Quality control; RNA-seq; Sequence data; fastq.

Fig. 1

Workflow for preprocessing fastq files with FastqPuri. Qreport generates a quality report in html format for each sample, while Sreport generates one summary quality report for all samples. Depending on the size of the sequence file with potential contaminations, makeTree or makeBloom generates a data structure for filtering contaminations. trimFilter (or trimFilterPE for paired-end data) filters and trims reads containing adapters or adapter remnants, biological contaminations and low quality bases. On the filtered reads, Qreport and Sreport can be run again to ensure that the filtered data meets the user’s expectations. Legend: yellow: fastq files, red: FastqPuri executables, green: FastqPuri quality reports in html format

Fig. 2

Graphics shown in Qreport. a Data set overview and basic statistics. b Per base sequence quality box plots. The blue line corresponds to the mean quality value. c Cycle average quality, per tile, per lane. d Nucleotide content per position. e Proportion of low quality bases, per tile, per lane. f Fraction of low quality bases {A, C, G, T} per position, per tile and per lane. g Proportion of bases with quality scores below different thresholds, for all tiles, all lanes. h Number of reads with m low quality bases

Fig. 3

Run times (user plus CPU time in seconds) of FastqPuri’s Qreport versus other tools for three different datasets. The datasets represent different quality encodings (Phred+33 and Phred+64) as well as different sequence name formats. Timings for SolexaQA++ on Illumina 1.3+ data are not shown because the smallest value was around 10 min and all other values became invisibly small on that scale

Fig. 4

Memory usage (in MB) of FastqPuri’s Qreport versus other tools for three different datasets. The datasets represent different quality encodings (Phred+33 and Phred+64) as well as different sequence name formats

Fig. 5

Run times (user plus CPU time in seconds) of FastqPuri’s trimFilter and trimFilterPE to remove adapter sequences versus fastp and trimmomatic

Fig. 6

Run times (user plus CPU time in seconds) and memory usage (in GB) of FastqPuri’s trimFilter and RNA-QC-Chain to remove reads from human rRNA transcripts